There are some geometric-based methods [1]}, [2]} to tackle the LiDAR-MOS problem, they do not need training data and procedure but occasionally result in incomplete or inaccurate detection of moving objects.
There are map cleaning-based methods that can be used to separate moving objects from static LiDAR maps. For example, Kim et al.  [3]} exploit the consistency check between the query scan and the pre-built map to remove dynamic points.
The map is then refined using a multi-resolution false prediction reverting algorithm.
Lim et al.  [4]} remove dynamic objects by checking the occupancy of each sector of LiDAR scans and then revert the ground plane by region growth. In contrast, Arora et al.  [5]} segment the ground plane and then remove the “ghost effect” caused by the moving object during mapping.
Chen et al.  [6]} use the map cleaning method with clustering and multi-object tracking to track the trajectories of different objects and generate training labels for LiDAR-MOS based on the tracking results.
Even though such map cleaning methods can distinguish moving and static objects, they usually can only run offline and are not suitable for online MOS.
